Metabolomics: Markers of Drug-Induced Liver Injury
Metabolomics: Markers of Drug-Induced Liver Injury
批准号:
7140610
负责人:
SUSAN J SUMNER
金额:
$43.82万
依托单位国家:
美国
项目类别:
财政年份:
2005
资助国家:
美国
项目状态:
已结题
起止时间:
2005-09-23 至 2009-07-31
关键词:
NIH Roadmap Initiative tagbiomarkerdrug adverse effectdrug screening /evaluationgas chromatography mass spectrometrygene expression profilinghepatotoxinlaboratory mouselaboratory ratliquid chromatography mass spectrometryliver disordermetabolomicsnuclear magnetic resonance spectroscopytoxicant screeningurinalysis
中文摘要
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英文摘要
DESCRIPTION (provided by applicant):
The cost of developing new drug entities for clinical use is substantial, and is greatly impacted by failure during the later stages of drug development. The early removal from the development pipeline of drug candidates that are likely to be toxic at therapeutic doses in humans has a huge impact on the cost of drug development. Liver injury is one of the major reasons for removal of a drug from the market, or addition of safety alerts. One aim of this proposal is to identify a set of endogenous metabolites excreted in urine that can be used to screen, as an early marker, for drug-induced liver injury. A second aim of this proposal is to gain more insight into markers that are reflective of specific mechanisms of liver injury. Both aims have the potential of better defining sensitive markers for pre-clinical use as well as provide potential for development of markers for patient populations. NMR and GC-MS metabolomic profiles will be developed for liver and urine from rats administered vehicle, no-effect levels, or drug induced-liver injury levels of clofibrate, valproic acid, isoniazid, phenytoin, and acetaminophen. Conventional measures of liver injury (liver weights, elevation in serum enzymes, and histopathology) will be obtained in addition to metabolomic profiles. The urine and liver metabolomics profiles for these drugs will be reduced and analyzed to provide the pattern of signals that are predictive of the response measurements for each drug. In addition, the union of the predictive patterns for individual drugs will be used to differentiate all groups based on the drug administered, dose level, exposure duration, and correlation with adverse response. The method used to identify the sub-set(s) of signals will be cross-validated by the drop-one-out approach. The signals defining these patterns will be identified using GC-MS, NMR, and LC-MS/MS methods and then assigned to biochemical pathways for defining the relevancy of the marker profiles to mode of action.
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